MIT Develops System For Tracking People 20 Times More Accurate Than Current WiFi Tracking Systems

MIT has always been known for making great contributions to science and technology over the years. MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has devised a new technology called Chronos which can detect the position of a person or object within a room using only WiFi signals accurately within tens of centimetres. The precision is commendable.

Chronos doesn’t need any secondary sensors and uses time-of-flight calculation which relies on time taken by data to travel from the WiFi access point to the device of the user. This new system is up to 20 times more accurate than the current WiFi-based tracking systems. It was around 94 percent accurate in detecting the room in which a person was situated and told with about 97 percent accuracy whether a shop’s customer was inside or outside the store.

According to researchers, various coffee shops, and stores can benefit from this technology as it would allow them to have password-less WiFi connections for their clients, and prevent the nearby users from using their connection. Also, since it can locate persons with a precision of up to tens of centimetres, it can be mounted on drones to survey indoor locations while at the same time not disturbing the inmates of the room.

Chronos is more precise and accurate than current triangulation-based systems. It needs only one WiFi access point to operate. Previous WiFi tracking systems required minimum four access points, which worked three at a time in various combinations to perform triangulation operations which detected a person’s position in the room.

Researchers have said that they were able to detect each user’s distance to the central WiFi access point by multiplying the time-in-flight value received from every user coming at the speed of light. Since WiFi devices work on various frequencies, Chronos often switches the frequencies to double-check the calculated values.

Researchers have reduced various nuisance factors like the background data coming from WiFi signals which are reflected by the nearby objects and several other factors to get more precise results. You can read the full research paper and even watch the presentation of the researchers at the USENIX Symposium on Networked Systems Design and Implementation (NSDI ’16).